There has been ground-breaking success of deep learning techniques for many tasks such as object detection and human action recognition [1]}, [2]}, which provides a great opportunity for developing deep learning based methods to accurately characterize FoG patterns from footstep pressure sequences.
In this scheme, pressure mat based FoG detection can be formulated as a sequence to sequence task where a pressure mat sequence is mapped into a FoG output sequence through temporal neural networks such as recurrent neural networks (RNNs). However, considering that the variations among all subjects (inter-subject variance) could be higher than the variance between FoG and non-FoG events (inter-class variance), there could be the potential issues to directly adopt these deep architectures as they were devised for general tasks often involving high inter-class variance.
For subject-level clinical applications including FoG detection, such large inter-subject variance of the feature vector (representation) could negatively impact the model performance when applying the trained model to unseen subjects due to the potential of over-fitting risk on subject specified patterns.
Hence, it is critical to devise proper mechanisms to reduce the inter-subject variance in pursuit of deriving the subject-independent FoG representation.
